Cost Analysis: SaaS Subscription vs Pay-Per-Usage

Why paying for compute is 10x cheaper than paying for a dashboard seat.

Cost Analysis: SaaS vs Serverless

When buying data tools, you’re usually paying for two things:

1. The Data (Compute/Storage)
2. The UI (Dashboard/Charts)

For engineers, paying for the UI is wasted budget. Let’s break down the economics of keyword research.

The SaaS Model (AnswerThePublic.com)

• Plan: Pro
• Cost: ~$99/month
• Limits: Daily search caps apply.
• Value: You pay for the convenience of the visualization wheel.

The Serverless Model (Apify Actor)

• Plan: Usage-based
• Cost: ~$0.25 per 1,000 results (estimated compute).
• Limits: None.
• Value: You pay only for the machine time required to scrape Google.

The Math

If you run 10 searches a month:

• SaaS: $99 / 10 =$9.90 per search.
• Apify: $0.05 per search.

Winner: Apify (by a landslide).

If you run 10,000 searches a month (Enterprise scale):

• SaaS: You need a custom Enterprise plan ($$$$).
• Apify: You just pay for more compute. The unit economics stay roughly the same.

Hidden Costs of SaaS

1. Seat Licenses: Need your whole team to access data? Pay for more seats. With Apify, you just share the API token or the output dataset.
2. Export Friction: Paying an intern to manually click “Download CSV” 50 times is a hidden labor cost. Automation eliminates this.
3. Unused Capacity: If you don’t use the tool for a week, you still pay the $99 subscription. With Serverless,$0 usage = $0 cost.

Conclusion

Subscriptions are for tools you live in (like Slack or Jira). For data utilities, pay-per-usage is the only logical model.